MOT ADHD: Behavioral data analysis
Ben Cowley (adapted from Sami Karadeniz)
Setup
Preprocessing
Pruning
Discarding subjects before preprocessing. First, we use the following criteria for choosing the subjects to be preprocessed in the first place:
- subjects have been measured with the photodetector
- subjects have the full number of usable sets (= 4)
- subjects do not have too many trial-rejections
Trim RT distributions?
Let’s take a look at the reaction time distribution for each subject.
It appears that provided data has already been trimmed between 200 and 900ms! If we believe that the RT distribution tails reflect theoretically invalid processes, we could justify trimming them to some theoretical limit, E.g. 180 ms (which is mean minus 2SD, based on Table 2 from Woods, et al., 2015 Front Hum Neurosci. 2015; 9:131).
However, we actually have no strong motivation for this approach, since
- it is not that clear where the threshold should be
- we aim to use parametric test statistics (based on fitting ex-gaussian distribution), for which it is better to have the original data for fitting and rely on the robustness of the statistics to account for outliers.
In summary, if we can get the untrimmed RTs, I would prefer to use that.
Generate IVs
Results
Results are given for each task x distractor state combination at the group and single subject levels. For each DV below, we first show QQ-plots and conditional distributions/boxplots/bar-charts, and test the effect of response handedness for Controls. We then test the main effects and interactions with repeated-measures ANOVA and also LMM models.
We perform contrast analysis using ‘emmeans’ package: this provides an ANOVA-like table from F-contrasts for each combination of factors. Any specific contrast not included in these combinations is provided by general linear hypothesis test (GLHT) with custom-defined coefficients for the LMM model DV = ALL x ALL.
Speed
- reaction time mean, estimated from ex-gaussian fit (mu of the gaussian part)
- reaction time variability, estimated from ex-gaussian fit (sigma of the gaussian part)
- reaction time slowing, estimated from long-tail of ex-gaussian fit (tau)
Accuracy
hit rates
false alarm rates
distractor effects (calculated in the following way)
\[\frac{HR_{distractors \ absent} - HR_{distractors \ present}} {HR_{distractors \ absent} + HR_{distractors \ present}}\]
MU
Ex-Gaussian stats of Reaction time - mu (gaussian central estimate)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$mu and exgauss[Group == "Control" & dom_resp == TRUE, ]$mu
## D = 0.23333, p-value = 0.254
## alternative hypothesis: two-sidedStatistics for RT ex-gauss mu
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 2.95e+00 9.74e-02 8.33e-02
## 3 Task 2 54 8.90e-01 4.17e-01 2.58e-03
## 5 Distractors 1 27 5.47e+01 5.85e-08 * 7.37e-02
## 4 Group:Task 2 54 2.44e-01 7.84e-01 7.10e-04
## 6 Group:Distractors 1 27 1.86e-06 9.99e-01 2.69e-09
## 7 Task:Distractors 2 54 8.47e+00 6.32e-04 * 1.55e-02
## 8 Group:Task:Distractors 2 54 1.47e+00 2.38e-01 2.74e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.861 0.1435
## 4 Group:Task 0.861 0.1435
## 7 Task:Distractors 0.808 0.0623
## 8 Group:Task:Distractors 0.808 0.0623
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF] p[HF]<.05
## 3 Task 0.878 0.40549 0.934 0.41087
## 4 Group:Task 0.878 0.75568 0.934 0.76943
## 7 Task:Distractors 0.839 0.00137 * 0.888 0.00108 *
## 8 Group:Task:Distractors 0.839 0.24002 0.888 0.23967
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.3497415
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.02739484 0.25083877
## Deg. of Freedom 1 27
##
## Residual standard error: 0.09638633
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000771667 0.000214281 0.023690890
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.02094565
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.024009408 0.000000001 0.011826235
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.02092866
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.004629319 0.000827573 0.015172424
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01676218
## Estimated effects may be unbalancedConstrasts for exgauss mu
Joint tests (F-contrasts) with facet line plot of interactions
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"
## model term df1 df2 F.ratio p.value
## Group 1 27 2.949 0.0974
## Distractors 1 27 54.749 <.0001
## Task 2 54 0.890 0.4167
## Group:Distractors 1 27 0.000 0.9989
## Group:Task 2 54 0.244 0.7842
## Distractors:Task 2 54 8.470 0.0006
## Group:Distractors:Task 2 54 1.473 0.2384## [1] "Split by GROUP:"## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 26.452 <.0001
## Task 2 54 0.711 0.4955
## Distractors:Task 2 54 8.220 0.0008
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 28.363 <.0001
## Task 2 54 0.412 0.6643
## Distractors:Task 2 54 1.491 0.2343## [1] "Contrast GROUP over all conditions:"## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 1.054 0.3111
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 3.224 0.0804
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 2.976 0.0925
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 1.838 0.1831
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 3.795 0.0587
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 2.392 0.1301## [1] "Split by DISTRACTOR:"## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 29.54 2.817 0.1038
## Task 2 103.05 6.109 0.0031
## Group:Task 2 103.05 1.196 0.3066
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 29.54 2.815 0.1039
## Task 2 103.05 1.589 0.2090
## Group:Task 2 103.05 0.252 0.7779## [1] "Contrast DISTRACTOR over all conditions:"## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 41.507 <.0001
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 3.787 0.0553
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 3.324 0.0722
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 23.222 <.0001
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 8.426 0.0048
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 8.137 0.0056## [1] "Split by TASK:"## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 2.187 0.1489
## Distractors 1 77.19 63.708 <.0001
## Group:Distractors 1 77.19 1.652 0.2025
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 2.606 0.1162
## Distractors 1 77.19 11.672 0.0010
## Group:Distractors 1 77.19 0.381 0.5389
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 3.353 0.0763
## Distractors 1 77.19 10.845 0.0015
## Group:Distractors 1 77.19 0.450 0.5043## [1] "Contrast TASK over all conditions:"## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 5.965 0.0035
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 1.320 0.2715
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 1.175 0.3130
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 0.492 0.6128Interaction of distractor state by Task:Full vs Right+Left is highly significant for CTRL, in contrast to ADHD.
## Contrast of DISTRACTOR x TASK(Full vs Right+Left) for CONTROL, ADHD, and between
## 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0
## 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25
## 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## drAF_ctrl == 0 -0.01573
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 12.3 1 160 0.0005895##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## drAF_adhd == 0 -0.006471
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.23 1 160 0.1373##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## drAF_CvA == 0 -0.004628
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.203 1 160 0.1397MU summary
CTRL consistently slower than ADHD (not due to response hand effect), with largest difference in ‘Task = attend-Right’ and in ‘Distractor = Present, Task = attend-Full’. Of note, Distractors affect CTRL weakly in Task = attend-Left/Right (F = 3.8, 3.3), but strongly in Task = attend-Full (F = 41.5), causing a strong interaction effect (F = 12.3) not present in ADHD (F = 2.2).
SIGMA
Ex-Gaussian stats of Reaction time - sigma (gaussian dispersion)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$sigma and exgauss[Group == "Control" & dom_resp == TRUE, ]$sigma
## D = 0.2381, p-value = 0.2342
## alternative hypothesis: two-sidedStatistics for RTV - exgauss sigma:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 4.372677168 0.04606248 * 9.189650e-02
## 3 Task 2 54 1.249324307 0.29485187 8.298936e-03
## 5 Distractors 1 27 5.795958889 0.02316748 * 2.348284e-02
## 4 Group:Task 2 54 0.138527943 0.87094732 9.270455e-04
## 6 Group:Distractors 1 27 0.001054741 0.97433074 4.376120e-06
## 7 Task:Distractors 2 54 2.065145049 0.13669867 6.252890e-03
## 8 Group:Task:Distractors 2 54 1.097591422 0.34100137 3.333075e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9994394 0.9927361
## 4 Group:Task 0.9994394 0.9927361
## 7 Task:Distractors 0.9754472 0.7238507
## 8 Group:Task:Distractors 0.9754472 0.7238507
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9994397 0.2948431 1.079324 0.2948519
## 4 Group:Task 0.9994397 0.8708397 1.079324 0.8709473
## 7 Task:Distractors 0.9760356 0.1380059 1.051145 0.1366987
## 8 Group:Task:Distractors 0.9760356 0.3399814 1.051145 0.3410014
## p[HF]<.05
## 3
## 4
## 7
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.05401102
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00733327 0.04528077
## Deg. of Freedom 1 27
##
## Residual standard error: 0.04095199
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000604827 0.000067242 0.013105816
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01557884
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.001743079 0.000000317 0.008117886
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.01733962
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.000434113 0.000242342 0.005961450
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01050701
## Estimated effects may be unbalancedConstrasts for exgauss sigma
SIGMA by LMM - joint tests and facet line plot of interactions
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"
## model term df1 df2 F.ratio p.value
## Group 1 27 4.373 0.0461
## Distractors 1 27 5.796 0.0232
## Task 2 54 1.249 0.2949
## Group:Distractors 1 27 0.001 0.9743
## Group:Task 2 54 0.139 0.8709
## Distractors:Task 2 54 2.065 0.1367
## Group:Distractors:Task 2 54 1.098 0.3410## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 2.878 0.1013
## Task 2 54 0.740 0.4819
## Distractors:Task 2 54 2.967 0.0599
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 2.921 0.0989
## Task 2 54 0.645 0.5289
## Distractors:Task 2 54 0.096 0.9083## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 1.248 0.2682
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.749 0.0573
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.754 0.0571
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.293 0.0743
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.503 0.0658
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 1.539 0.2193## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 36.38 3.659 0.0637
## Task 2 94.70 2.956 0.0569
## Group:Task 2 94.70 0.527 0.5922
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 36.38 3.757 0.0604
## Task 2 94.70 0.053 0.9485
## Group:Task 2 94.70 0.350 0.7057## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 7.941 0.0064
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 0.779 0.3806
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 0.027 0.8707
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 2.494 0.1192
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 1.242 0.2693
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 1.439 0.2346## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 2.892 0.0962
## Distractors 1 63.97 9.758 0.0027
## Group:Distractors 1 63.97 0.864 0.3561
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 4.369 0.0425
## Distractors 1 63.97 1.986 0.1636
## Group:Distractors 1 63.97 0.019 0.8896
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 3.005 0.0901
## Distractors 1 63.97 0.905 0.3451
## Group:Distractors 1 63.97 0.513 0.4766## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 2.797 0.0660
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.076 0.9271
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.610 0.5453
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.336 0.7154Illustration of weak effect of Distractors per group, and interaction of Distractor and Task=Full vs Left/Right in CTRL
## CONTRAST main effect of distractor within ADHD 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## dstr_adhd == 0 -0.006248
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.361 1 160 0.03835## CONTRAST main effect of distractor within CTRL 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## dstr_ctrl == 0 -0.006419
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.296 1 160 0.0398## CONTRAST interaction of distractor and task levels in CTRL 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## drAF_ctrl == 0 -0.005718
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.03 1 160 0.08364SIGMA summary
Sigma is consistently more variable for CTRL than for ADHD (F = 4.4). This is true in all conditions (F = 3.3 - 3.8) except Distractors = Absent Task = attend-Full, and Distractors = Present Task = attend-Right. The difference is strongest in Task = attend-Left (F = 4.4).
Distractors have smaller effect here than for other DVs (F = 5.8), in fact the effect comes mainly from one condition, CTRL group Task = attend-Full (F = 7.9). Thus again the effect of Distractors on CTRL in attend-Full is unusually large (compared to ADHD or lateralised attending).
TAU
Ex-Gaussian stats of Reaction time - tau (exponential tail)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$tau and exgauss[Group == "Control" & dom_resp == TRUE, ]$tau
## D = 0.1619, p-value = 0.6877
## alternative hypothesis: two-sidedStatistics for RT ex-gauss tau:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.07631259 0.7844617 0.0010830259
## 3 Task 2 54 1.39517295 0.2565781 0.0089559367
## 5 Distractors 1 27 0.07716397 0.7832942 0.0004362719
## 4 Group:Task 2 54 0.62658427 0.5382542 0.0040421317
## 6 Group:Distractors 1 27 0.48632417 0.4915334 0.0027432474
## 7 Task:Distractors 2 54 0.80645202 0.4517426 0.0085521564
## 8 Group:Task:Distractors 2 54 0.37485044 0.6891666 0.0039934426
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9418604 0.4590133
## 4 Group:Task 0.9418604 0.4590133
## 7 Task:Distractors 0.9659766 0.6376254
## 8 Group:Task:Distractors 0.9659766 0.6376254
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9450549 0.2567726 1.014004 0.2565781
## 4 Group:Task 0.9450549 0.5297651 1.014004 0.5382542
## 7 Task:Distractors 0.9670961 0.4482268 1.040409 0.4517426
## 8 Group:Task:Distractors 0.9670961 0.6822427 1.040409 0.6891666
## p[HF]<.05
## 3
## 4
## 7
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.08739718
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.000059174 0.020936263
## Deg. of Freedom 1 27
##
## Residual standard error: 0.02784631
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000508445 0.000221509 0.009544999
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01329508
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.000019899 0.000150134 0.008335238
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.01757022
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.000493742 0.000218830 0.015762064
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01708479
## Estimated effects may be unbalanced## boundary (singular) fit: see ?isSingularConstrasts for exgauss tau
TAU by LMM - joint tests and facet line plot of interactions
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"
## model term df1 df2 F.ratio p.value
## Group 1 27 0.076 0.7845
## Distractors 1 27 0.077 0.7833
## Task 2 54 1.052 0.3561
## Group:Distractors 1 27 0.486 0.4915
## Group:Task 2 54 0.473 0.6259
## Distractors:Task 2 54 1.005 0.3729
## Group:Distractors:Task 2 54 0.467 0.6294## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 0.460 0.5036
## Task 2 54 0.308 0.7364
## Distractors:Task 2 54 0.050 0.9512
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 0.091 0.7650
## Task 2 54 1.250 0.2947
## Distractors:Task 2 54 1.470 0.2389## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.780 0.3788
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.202 0.6540
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.023 0.8796
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.184 0.6690
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.534 0.4663
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.205 0.6514## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 45.56 0.019 0.8905
## Task 2 108.00 1.742 0.1801
## Group:Task 2 108.00 0.939 0.3941
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 45.56 0.367 0.5477
## Task 2 108.00 0.315 0.7303
## Group:Task 2 108.00 0.000 0.9998## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.040 0.8417
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.211 0.6476
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.388 0.5354
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 1.793 0.1843
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.039 0.8431
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.935 0.3365## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.077 0.7831
## Distractors 1 79.54 0.618 0.4340
## Group:Distractors 1 79.54 1.155 0.2858
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.031 0.8606
## Distractors 1 79.54 0.037 0.8482
## Group:Distractors 1 79.54 0.219 0.6411
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.569 0.4536
## Distractors 1 79.54 1.254 0.2663
## Group:Distractors 1 79.54 0.050 0.8232## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.197 0.8214
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.161 0.8517
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 2.566 0.0815
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.154 0.8570ADHD has interaction of Distractor and Task: more slowing for attend-Right in Absent than in Present, in contrast to other Task conditions
## CONTRAST Task=attend-Full vs Left/Right when Distractor=Absent in ADHD 0 0.5 0 0 0 -0.25 0 0 0 -0.25 0 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## FvLR_Abs_adhd == 0 -0.005234
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.398 1 160 0.03756## CONTRAST Task=attendFull vs Left/Right in ADHD 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## drAR_adhd == 0 0.005102
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.089 1 160 0.1503TAU summary
There were no main effects or noticeable trends for TAU. The only noteworthy effect is Task=attend-Full vs Left/Right when Distractor=Absent in ADHD (F = 4.4). The visible difference in ADHD between Distractors’ effect in Task=attend-Right (reduced slowing) and the other Task conditions (increased slowing) does not match a notable effect (F = 2.1)
HIT RATES
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: hit_rates[Group == "Control" & dom_resp == FALSE, ]$hit_rate and hit_rates[Group == "Control" & dom_resp == TRUE, ]$hit_rate
## D = 0.48095, p-value = 0.0006095
## alternative hypothesis: two-sidedStatistics for hit rates
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 1.184201e-04 9.913975e-01 3.503582e-06
## 3 Task 2 54 4.150472e-01 6.623975e-01 1.069931e-03
## 5 Distractors 1 27 7.184181e+01 4.342724e-09 * 1.416731e-01
## 4 Group:Task 2 54 4.748406e-02 9.536654e-01 1.225230e-04
## 6 Group:Distractors 1 27 2.126028e-03 9.635628e-01 4.884545e-06
## 7 Task:Distractors 2 54 3.916128e+00 2.581213e-02 * 9.974818e-03
## 8 Group:Task:Distractors 2 54 2.198191e+00 1.208401e-01 5.623646e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9639195 0.6201978
## 4 Group:Task 0.9639195 0.6201978
## 7 Task:Distractors 0.9009637 0.2577478
## 8 Group:Task:Distractors 0.9009637 0.2577478
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9651760 0.65531325 1.0381051 0.66239749
## 4 Group:Task 0.9651760 0.94946032 1.0381051 0.95366544
## 7 Task:Distractors 0.9098881 0.02989337 * 0.9720671 0.02701283
## 8 Group:Task:Distractors 0.9098881 0.12613623 0.9720671 0.12246783
## p[HF]<.05
## 3
## 4
## 7 *
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.8621807
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.0000043 0.9758254
## Deg. of Freedom 1 27
##
## Residual standard error: 0.1901097
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.00132633 0.00014969 0.08511514
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03970147
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.20179408 0.00000597 0.07577765
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.05297716
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.01173242 0.00690856 0.08485660
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03964112
## Estimated effects may be unbalancedConstrasts for Hit Rates
Hit rate by LMM - joint tests and facet line plot of interactions
## NULL
## model term df1 df2 F.ratio p.value
## Group 1 27 0.000 0.9914
## Distractors 1 27 71.837 <.0001
## Task 2 54 0.416 0.6620
## Group:Distractors 1 27 0.002 0.9636
## Group:Task 2 54 0.048 0.9536
## Distractors:Task 2 54 3.910 0.0259
## Group:Distractors:Task 2 54 2.195 0.1212## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 35.100 <.0001
## Task 2 54 0.152 0.8595
## Distractors:Task 2 54 5.612 0.0061
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 36.797 <.0001
## Task 2 54 0.317 0.7296
## Distractors:Task 2 54 0.311 0.7342## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.215 0.6454
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.393 0.5344
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.019 0.8913
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.015 0.9022
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.103 0.7500
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.193 0.6627## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 31.17 0.000 0.9985
## Task 2 108.00 3.318 0.0399
## Group:Task 2 108.00 0.806 0.4492
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 31.17 0.001 0.9819
## Task 2 108.00 1.008 0.3684
## Group:Task 2 108.00 1.436 0.2424## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 42.230 <.0001
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 8.918 0.0038
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 7.388 0.0082
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 20.833 <.0001
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 13.026 0.0006
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 18.672 <.0001## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.008 0.9308
## Distractors 1 74.60 61.543 <.0001
## Group:Distractors 1 74.60 2.257 0.1372
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.000 0.9941
## Distractors 1 74.60 21.672 <.0001
## Group:Distractors 1 74.60 0.130 0.7199
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.004 0.9497
## Distractors 1 74.60 24.575 <.0001
## Group:Distractors 1 74.60 1.097 0.2983## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 3.439 0.0356
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 2.324 0.1027
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.587 0.5578
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.041 0.9600Distractor effect interacts with Task=Full vs Left/Right in CTRL, and the difference of this effect with ADHD is also noticeable
## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## drAF_ctrl == 0 0.0307
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 9.664 1 160 0.002225## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## drAF_adhd == 0 0.004873
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2609 1 160 0.6102## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## drAF_CvA == 0 0.01291
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.537 1 160 0.06182Hit Rate summary
CTRL performance was affected by response handedness, rendering this DV problematic for group comparison (even this result is counter-intuitive as the dominant-hand CTRL subgroup performed worse than the non-dominant hand subgroup). However, the most noteworthy result here is intra-group: CTRL show again a large interaction between Distractor effect and Task, with more impact of Distractors on Task=attend-Full than Left/Right (F = 9.7). The interaction is not present in ADHD (F = 0.3), such that the difference between groups in terms of this interaction is noteworthy (F = 3.5).
FALSE ALARMS
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: fa_rates[Group == "Control" & dom_resp == FALSE, ]$fa_rate and fa_rates[Group == "Control" & dom_resp == TRUE, ]$fa_rate
## D = 0.16667, p-value = 0.7159
## alternative hypothesis: two-sidedStatistics for false alarm rates:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.99624544 0.3270825057 0.0235676226
## 3 Task 2 54 1.47716918 0.2373600463 0.0082416656
## 5 Distractors 1 27 15.43837908 0.0005336195 * 0.0529696664
## 4 Group:Task 2 54 0.72569044 0.4886497069 0.0040659392
## 6 Group:Distractors 1 27 0.03775998 0.8473805867 0.0001367836
## 7 Task:Distractors 2 54 9.37433253 0.0003201517 * 0.0323030917
## 8 Group:Task:Distractors 2 54 0.04387023 0.9571122216 0.0001561948
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9653826 0.6325478
## 4 Group:Task 0.9653826 0.6325478
## 7 Task:Distractors 0.9583853 0.5754691
## 8 Group:Task:Distractors 0.9583853 0.5754691
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9665409 0.2378270702 1.039743 0.2373600463
## 4 Group:Task 0.9665409 0.4843929958 1.039743 0.4886497069
## 7 Task:Distractors 0.9600479 0.0003975696 * 1.031955 0.0003201517
## 8 Group:Task:Distractors 0.9600479 0.9524710628 1.031955 0.9571122216
## p[HF]<.05
## 3
## 4
## 7 *
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.9771982
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00274685 0.07444458
## Deg. of Freedom 1 27
##
## Residual standard error: 0.05250911
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000904047 0.000464614 0.017286406
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01789186
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.006351256 0.000015569 0.011132366
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.02030541
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.003792034 0.000017779 0.010941836
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.0142347
## Estimated effects may be unbalancedConstrasts for False Alarms
False Alarm by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 0.996 0.3271
## Distractors 1 27 15.438 0.0005
## Task 2 54 1.477 0.2374
## Group:Distractors 1 27 0.038 0.8474
## Group:Task 2 54 0.726 0.4887
## Distractors:Task 2 54 9.374 0.0003
## Group:Distractors:Task 2 54 0.044 0.9571## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 8.218 0.0079
## Task 2 54 2.013 0.1435
## Distractors:Task 2 54 4.949 0.0106
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 7.224 0.0122
## Task 2 54 0.125 0.8831
## Distractors:Task 2 54 4.452 0.0162## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 0.471 0.4964
## Distractors 1 71.58 16.822 0.0001
## Group:Distractors 1 71.58 0.116 0.7348
##
## Task = AttendNone:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 2.014 0.1636
## Distractors 1 71.58 0.062 0.8043
## Group:Distractors 1 71.58 0.000 0.9938
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 0.350 0.5573
## Distractors 1 71.58 20.414 <.0001
## Group:Distractors 1 71.58 0.007 0.9351False Alarm summary
There were no noteworthy effects of False Alarms: groups were not different in any condition; Distractor effects behaved as expected (large effect in Task = attend-Left/Right (F = 17/20), no effect in attend-None (F = 0.1)); there was an interaction between Distractor and Task.
DISTRACTOR EFFECT
## `summarise()` regrouping output by 'Group' (override with `.groups` argument)
Statistics for distractor effect:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.000181595 0.9893473 3.337908e-06
## 3 Task 2 54 3.703857225 0.0310894 * 6.463280e-02
## 4 Group:Task 2 54 2.241067418 0.1161461 4.013131e-02
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.8796146 0.1887128
## 4 Group:Task 0.8796146 0.1887128
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF] p[HF]<.05
## 3 Task 0.89255 0.0364544 * 0.9514771 0.0334055 *
## 4 Group:Task 0.89255 0.1225835 0.9514771 0.1190267
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.04157256
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00000044 0.06523006
## Deg. of Freedom 1 27
##
## Residual standard error: 0.04915209
## 2 out of 3 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.00864155 0.00549520 0.06620525
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03501461
## Estimated effects may be unbalanced## [1] "Control.AttendFull" "ADHD.AttendFull" "Control.AttendLeft"
## [4] "ADHD.AttendLeft" "Control.AttendRight" "ADHD.AttendRight"Contrasts for distractor effect:
## model term df1 df2 F.ratio p.value
## Group 1 27 0.000 0.9893
## Task 2 54 3.704 0.0311
## Group:Task 2 54 2.241 0.1161## Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 54 5.487 0.0068
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 54 0.278 0.7584## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 72.35 2.166 0.1454
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 72.35 0.179 0.6739
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 72.35 1.042 0.3107##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## FvLR_adhd == 0 0.005112
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2132 1 79 0.6455##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## FvLR_ctrl == 0 0.03795
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 10.96 1 79 0.001403##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## RvFL_ctrl == 0 -0.02008
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.069 1 79 0.0837## TEST CONTRAST: FvLR ctrl V ADHD 0.5 -0.5 -0.25 0.25 -0.25 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## FvLR_CvA == 0 0.01642
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.245 1 79 0.04265DE summary
There was no effect of group on Distractor effect. Task = attend-Full differed strongly from Left/Right for CTRL group (F = 11), and this induced a between groups difference with ADHD in the same Task contrast (F = 4.2)